


import math_equivalence  # From: git+https://github.com/hendrycks/math.git

import datasets


__UpperCamelCase     		=							'\\n@article{hendrycksmath2021,\n  title={Measuring Mathematical Problem Solving With the MATH Dataset},\n  author={Dan Hendrycks\n    and Collin Burns\n    and Saurav Kadavath\n    and Akul Arora\n    and Steven Basart\n    and Eric Tang\n    and Dawn Song\n    and Jacob Steinhardt},\n  journal={arXiv preprint arXiv:2103.03874},\n  year={2021}\n}\n'


__UpperCamelCase     		=							'\\nThis metric is used to assess performance on the Mathematics Aptitude Test of Heuristics (MATH) dataset.\nIt first canonicalizes the inputs (e.g., converting "1/2" to "\\frac{1}{2}") and then computes accuracy.\n'


__UpperCamelCase     		=							R'\nCalculates accuracy after canonicalizing inputs.\n\nArgs:\n    predictions: list of predictions to score. Each prediction\n        is a string that contains natural language and LaTex.\n    references: list of reference for each prediction. Each\n        reference is a string that contains natural language\n        and LaTex.\nReturns:\n    accuracy: accuracy after canonicalizing inputs\n        (e.g., converting "1/2" to "\\frac{1}{2}")\n\nExamples:\n    >>> metric = datasets.load_metric("competition_math")\n    >>> results = metric.compute(references=["\\frac{1}{2}"], predictions=["1/2"])\n    >>> print(results)\n    {\'accuracy\': 1.0}\n'







@datasets.utils.file_utils.add_end_docstrings(_DESCRIPTION       ,						_KWARGS_DESCRIPTION   )
class 							lowerCamelCase__						(				datasets.Metric   ):






			"""simple docstring"""






			def 					snake_case__					(				self  ):

						'''simple docstring'''


						return datasets.MetricInfo(
						    description=_DESCRIPTION   ,    citation=_CITATION   ,    inputs_description=_KWARGS_DESCRIPTION   ,    features=datasets.Features(
						        {
						            "predictions": datasets.Value("string"  ),
						            "references": datasets.Value("string"  ),
						        }  )   ,    homepage="https://github.com/hendrycks/math"   ,    codebase_urls=["https://github.com/hendrycks/math"]   ,    )







			def 					snake_case__					(				self   ,    snake_case   ,    snake_case  ):

						'''simple docstring'''


						UpperCamelCase__       =							0.0
						for i, j in zip(snake_case   ,    snake_case  ):
									n_correct += 1.0 if math_equivalence.is_equiv(snake_case   ,    snake_case  ) else 0.0
						UpperCamelCase__       =							n_correct / len(snake_case  )
						return {
						    "accuracy": accuracy,
						}






from typing import Callable, Optional

from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream







class 							lowerCamelCase__						(				UpperCAmelCase   ):






			"""simple docstring"""






			def __init__(				self   ,    snake_case   ,    snake_case = None   ,    snake_case = None   ,    snake_case = False   ,    snake_case = False   ,    snake_case = None   ,    snake_case = None   ,    **snake_case   ,    ):

						'''simple docstring'''


						super().__init__(
						    features=snake_case   ,    cache_dir=snake_case   ,    keep_in_memory=snake_case   ,    streaming=snake_case   ,    num_proc=snake_case   ,    **snake_case   ,    )
						UpperCamelCase__       =							Generator(
						    cache_dir=snake_case   ,    features=snake_case   ,    generator=snake_case   ,    gen_kwargs=snake_case   ,    **snake_case   ,    )







			def 					snake_case__					(				self  ):

						'''simple docstring'''


						if self.streaming:
									UpperCamelCase__       =							self.builder.as_streaming_dataset(split="train"  )
						# Build regular (map-style) dataset
						else:
									UpperCamelCase__       =							None
									UpperCamelCase__       =							None
									UpperCamelCase__       =							None
									UpperCamelCase__       =							None

									self.builder.download_and_prepare(
									    download_config=snake_case   ,    download_mode=snake_case   ,    verification_mode=snake_case   ,    base_path=snake_case   ,    num_proc=self.num_proc   ,    )
									UpperCamelCase__       =							self.builder.as_dataset(
									    split="train"   ,    verification_mode=snake_case   ,    in_memory=self.keep_in_memory  )
						return dataset


